Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak.

Predicting the outbreak of a pandemic is an important measure in order to help saving people lives threatened by Covid-19. Having information about the possible spread of the pandemic, authorities and people can make better decisions. For example, such analyses help developing better strategies for distributing vaccines and medicines. This paper has modified the original Susceptible-Infectious-Recovered (SIR) model to Susceptible-Immune-Infected-Recovered (SIRM) which includes the Immunity ratio as a parameter to enhance the prediction of the pandemic. SIR is a widely used model to predict the spread of a pandemic. Many types of pandemics imply many variants of the SIR models which make it very difficult to find out the best model that matches the running pandemic. The simulation of this paper used the published data about the spread of the pandemic in order to examine our new SIRM. The results showed clearly that our new SIRM covering the aspects of vaccine and medicine is an appropriate model to predict the behavior of the pandemic.

Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak

Predicting the outbreak of a pandemic is an important measure in order to help saving people lives threatened by Covid-19. Having information about the possible spread of the pandemic, authorities and people can make better decisions. For example, such analyses help developing better strategies for distributing vaccines and medicines. This paper has modified the original Susceptible-Infectious-Recovered (SIR) model to Susceptible-Immune-Infected-Recovered (SIRM) which includes the Immunity ratio as a parameter to enhance the prediction of the pandemic. SIR is a widely used model to predict the spread of a pandemic. Many types of pandemics imply many variants of the SIR models which make it very difficult to find out the best model that matches the running pandemic. The simulation of this paper used the published data about the spread of the pandemic in order to examine our new SIRM. The results showed clearly that our new SIRM covering the aspects of vaccine and medicine is an appropriate model to predict the behavior of the pandemic.

Everyday capabilities were a path to resilience during COVID-19: A case study of five countries.

COVID-19 demanded urgent responses by all countries, with wide variations in the scope and sustainability of those responses. Scholarship on resilience has increasingly emphasized relational considerations such as norms and power and how they influence health systems' responses to evolving challenges. In this study, we explored what influenced countries' national pandemic responses over time considering a country's capacity to test for COVID-19. To identify countries for inclusion, we used daily reports of COVID-19 cases and testing from 184 countries between January 21st, 2020 to December 31st, 2020. Countries reporting test data consistently and for at least 105 days were included, yielding a sample of 52 countries. We then sampled five countries representing different geographies, income levels, and governance structures (Belgium, Ethiopia, India, Israel, Peru) and conducted semi-structured key informant interviews with stakeholders working in, or deeply familiar, with national responses. Across these five countries, we found that existing health systems capacities and political leadership determined how responses unfolded, while emergency plans or pandemic preparedness documents were not fit-for-purpose. While all five countries were successful at reducing COVID-19 infections at a specific moment in the pandemic, political economy factors complicated the ability to sustain responses, with all countries experiencing larger waves of the virus in 2021 or 2022. Our findings emphasize the continued importance of foundational public health and health systems capacities, bolstered by clear leadership and multisectoral coordination functions. Even in settings with high-level political leadership and a strong multisectoral response, informants wished they-and their country's health system-were more prepared to address the pandemic and maintain an effective response over time. Our findings challenge emergency preparedness as the dominant frame in pandemic preparedness and call for a continued emphasis on health systems strengthening to respond to future health shocks-and a pandemic moving to endemic status.

Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach.

Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms. Using topic-modelling techniques, we identified the impacting factors (predictors) and categorised them into two major dimensions based on strategic adoption and motivational association. Findings from our study suggest that time and money, convenience, responsiveness, and availability emerge as significant predictors for delivering a positive user experience on m-health platforms. Next, we identified substantial moderating effects of review polarity on the predictors related to brand association and hedonic motivation, such as online booking and video consultation. Further, we also identified the top predictors for successful user experience across these platforms. Recommendations from our study will benefit business managers by offering an improved service design leading to higher user satisfaction across these m-health platforms.

How Does Misinformation and Capricious Opinions Impact the Supply Chain - A Study on the Impacts During the Pandemic.

Misinformation or fake news has had multifaceted ramifications with the onset of the Covid-19 pandemic, creating widespread panic amongst people. This study investigates the impact of misinformation/ fake news (on internet platforms) on consumer buying behavior, impact of fear (created by fake news) on hoarding of essential products and consumer spending and finally impact of misinformation-induced panic buying on supply chain disruptions. It draws upon the consumer decision theory and the cognitive load theory for explaining the psychological and behavioral responses of consumers. The study follows an inductive approach towards theory building using a multi-method approach. Initially, a qualitative research method based on interviews followed by text-mining has been used followed by analysis using python for topic modelling using Latent Dirichlet Allocation (LDA). The findings revealed several prominent themes like consumer shift to online buying, two contrasting spending intentions namely financial security and compensatory consumptions, irrational panic buying, uncertainty/ambiguity of government protocol and norms, social media fraudulent practices and misinformation dissemination, personalized buying experience, reduced trust on news and marketers, logistics and transportation bottlenecks, labor shortage due to migration and plant closures, and bullwhip effect in supply chains.

A Confirmation Bias View on Social Media Induced Polarisation During Covid-19.

Social media has played a pivotal role in polarising views on politics, climate change, and more recently, the Covid-19 pandemic. Social media induced polarisation (SMIP) poses serious challenges to society as it could enable 'digital wildfires' that can wreak havoc worldwide. While the effects of SMIP have been extensively studied, there is limited understanding of the interplay between two key components of this phenomenon: confirmation bias (reinforcing one's attitudes and beliefs) and echo chambers (i.e., hear their own voice). This paper addresses this knowledge deficit by exploring how manifestations of confirmation bias contributed to the development of 'echo chambers' at the height of the Covid-19 pandemic. Thematic analysis of data collected from 35 participants involved in supply chain information processing forms the basis of a conceptual model of SMIP and four key cross-cutting propositions emerging from the data that have implications for research and practice.

Roles of Mobilized Controls and Environmental Uncertainty on Supply Chain Resilience: An Empirical Study From Dynamic-Capabilities-View and Levers-of-Control Perspectives

COVID-19 creates big challanges to supply chain management. This article empirically examines the impact of management control systems (MCS) on managing supply chain resilience (SCR) to enhance organizational competitiveness under environmental uncertainty. Drawing on the dynamic capabilities view (DCV) theory and levers of control (LoC) framework, an industrial survey was conducted. Analyses performed on the collected data from 405 manufacturing firms reveal that the effects of MCS on SCR and organizational competitiveness depend on the nature and use of mobilized controls. Different from conventional wisdom, the study suggests that belief, boundary, and interactive systems enable firms to achieve SCR and these systems have positive effects on organizational competitiveness through SCR. Moreover, interestingly, diagnostic systems seem to play no role in strengthening SCR and organizational competitiveness. The study thus argues that firms should employ the enabling characteristics of belief and interactive systems, along with the controlling features of boundary systems to manage SCR and ultimately be more organizational-competitive. The research also uncovers that environmental uncertainty positively moderates the indirect effects of MCS on organizational competitiveness through SCR. Indeed, the study indicates that firms evolving in highly uncertain and dynamic environments tend to increase the use of different MCS to generate detailed information that is essential to strengthen SCR. Overall, this theory-based empirical research provides novel insights regarding how MCS would contribute to improving SCR and organizational competitiveness, especially under disruptions such as COVID-19.

Quantum computing led innovation for achieving a more sustainable Covid-19 healthcare industry

Involvement of multiple stakeholders in healthcare industry, even the simple healthcare problems become complex due to classical approach to treatment. In the Covid-19 era where quick and accurate solutions in healthcare are needed along with quick collaboration of stakeholders such as patients, insurance agents, healthcare providers and medicine supplier etc., a classical computing approach is not enough. Therefore, this study aims to identify the role of quantum computing in disrupting the healthcare sector with the lens of organizational information processing theory (OIPT), creating a more sustainable (less strained) healthcare system. A semi-structured interview approach is adopted to gauge the expectations of professionals from healthcare industry regarding quantum computing. A structured approach of coding, using open, axial and selective approach is adopted to map the themes under quantum computing for healthcare industry. The findings indicate the potential applications of quantum computing for pharmaceutical, hospital, health insurance organizations along with patients to have precise and quick solutions to the problems, where greater accuracy and speed can be achieved. Existing research focuses on the technological background of quantum computing, whereas this study makes an effort to mark the beginning of quantum computing research with respect to organizational management theory.

COVID-19 cases and their outcome among patients with uncommon co-existing illnesses: A lesson from Northern India

Introduction Newer coexisting conditions should be identified in order to modify newer risk factors. Aim was to identify patients with non-classical or less common coexisting conditions in patients infected of COVID 19. Method Single centred study from June 2020 to May 2021 at a tertiary centre in North India. A preformed questionnaire was used to record clinical and laboratory parameters and to identify cases which are in addition to CDC list and Indian data. Results 0.67% (46) cases out of 6832 patients were identified to have non-classical coexisting illness. It was divided into 2 groups-infections A (60.1%) and non-infections B (39.9%). Group A included-tuberculosis- pulmonary (14.3%) & extra pulmonary (32.9%), bacterial (25.0%) viral infections [dengue, hepatitis B & C] (14.3%), HIV disease (10.7%) and malaria (3.6%). Group B included- organ transplant (27.8%), autoimmune [myasthenia gravis, polymyositis, psoriasis] (22.6%), haematologic [Haemophilia, ITP, Aplastic anaemia, APML, CML] (27.8%), uncommon malignancies [disseminated sacral chordoma and GTN] (11.1%) and snakebite (11.1%). Serum Procalcitonin was not helpful for diagnosis of bacterial infection in COVID-19 disease. Group A had significantly longer duration of illness, hepatitis and elevated CRP. The mortality in group A & B were 32.1% and 43.8% respectively. Death in non-severe COVID cases was in tetanus and snakebite. 30.7% death among tuberculosis patients. More than 70% of deaths were attributable to COVID 19 in both the groups. Conclusion In Indian settings, comorbidities like tuberculosis and bacterial infections can precipitate severe COVID 19 unlike other parts of the world where tuberculosis is relatively uncommon.

AI technologies and their impact on supply chain resilience during COVID-19

Purpose>COVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approach>We adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.Findings>An AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implications>As the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implications>Supply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/value>The present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.

Has Covid-19 accelerated opportunities for digital entrepreneurship? An Indian perspective.

Covid-19 has challenged many businesses to orient themselves towards digital solutions for their survival. Due to the rising digital wave during Covid-19, there has been a plethora of opportunities for aspiring entrepreneurs to enter the market. Hence, this study focuses on understanding emerging areas and technologies for digital entrepreneurship. This study adopted a qualitative approach with semi-structured interviews through the lens of the diffusion of innovations theory. A total of 23 entrepreneurs responded and presented their views on Covid-19-induced opportunities for digital entrepreneurship. A structured process of open, axial, and selective coding was adopted for the thematic analysis. The study presents a framework based on four promising propositions. Results of the thematic analysis indicate the emergence of digital entrepreneurship opportunities in technology (EdTech, FinTech, cybersecurity), healthcare (diagnostics, virtual care, fitness), entertainment (over the top, gaming, social media), and e-commerce (contactless delivery, payment methods, augmented reality). In this study, entrepreneurs presented their views based on their experience with the platform or technology they operated. To this end, the present study offers implications both for scholars and entrepreneurs working in and aspiring to digital entrepreneurship along with future scope of research.

Integrated health service delivery during COVID-19: a scoping review of published evidence from low-income and lower-middle-income countries.

BACKGROUND: Integrated health service delivery (IHSD) is a promising approach to improve health system resilience. However, there is a lack of evidence specific to the low/lower-middle-income country (L-LMIC) health systems on how IHSD is used during disease outbreaks. This scoping review aimed to synthesise the emerging evidence on IHSD approaches adopted in L-LMIC during the COVID-19 pandemic and systematically collate their operational features. METHODS: A systematic scoping review of peer-reviewed literature, published in English between 1 December 2019 and 12 June 2020, from seven electronic databases was conducted to explore the evidence of IHSD implemented in L-LMICs during the COVID-19 pandemic. Data were systematically charted, and key features of IHSD systems were presented according to the postulated research questions of the review. RESULTS: The literature search retrieved 1487 published articles from which 18 articles met the inclusion criteria and included in this review. Service delivery, health workforce, medicine and technologies were the three most frequently integrated health system building blocks during the COVID-19 pandemic. While responding to COVID-19, the L-LMICs principally implemented the IHSD system via systematic horizontal integration, led by specific policy measures. The government's stewardship, along with the decentralised decision-making capacity of local institutions and multisectoral collaboration, was the critical facilitator for IHSD. Simultaneously, fragmented service delivery structures, fragile supply chain, inadequate diagnostic capacity and insufficient workforce were key barriers towards integration. CONCLUSION: A wide array of context-specific IHSD approaches were operationalised in L-LMICs during the early phase of the COVID-19 pandemic. Emerging recommendations emphasise the importance of coordination and integration across building blocks and levels of the health system, supported by a responsive governance structure and stakeholder engagement strategies. Future reviews can revisit this emerging evidence base at subsequent phases of COVID-19 response and recovery in L-LMICs to understand how the approaches highlighted here evolve.

Supply chain vulnerability assessment for manufacturing industry.

In today's business, environment natural and manmade disasters like recent event (Covid 19) have increased the attention of practitioners and researchers to Supply chain vulnerability. Purpose of this paper is to investigate and prioritize the factors that are responsible for supply chain vulnerability. Extant literature review and interviews with the experts helped to extract 26 supply chain vulnerability factors. Further, the relative criticality of vulnerability factors is assessed by analytical hierarchy process (AHP). Critical part supplier; location of supplier; long supply chain lead times; Fixing process owners and mis-aligned incentives in supply chain are identified as the most critical factors among twenty-six vulnerability factors. Research concludes that not only long and complex supply chain but supply chain practices adopted by firms also increase supply chain vulnerability. Relative assessment of vulnerability factors enables professionals to take appropriate mitigation strategies to make the supply chains more robust. This research adds in building a model for vulnerability factors that are internal to supply chain & controllable.

Infection vulnerability stratification risk modelling of COVID-19 data: a deterministic SEIR epidemic model analysis.

Basic Susceptible-Exposed-Infectious-Removed (SEIR) models of COVID-19 dynamics tend to be excessively pessimistic due to high basic reproduction values, which result in overestimations of cases of infection and death. We propose an extended SEIR model and daily data of COVID-19 cases in the U.S. and the seven largest European countries to forecast possible pandemic dynamics by investigating the effects of infection vulnerability stratification and measures on preventing the spread of infection. We assume that (i) the number of cases would be underestimated at the beginning of a new virus pandemic due to the lack of effective diagnostic methods and (ii) people more susceptible to infection are more likely to become infected; whereas during the later stages, the chances of infection among others will be reduced, thereby potentially leading to pandemic cessation. Based on infection vulnerability stratification, we demonstrate effects brought by the fraction of infected persons in the population at the start of pandemic deceleration on the cumulative fraction of the infected population. We interestingly show that moderate and long-lasting preventive measures are more effective than more rigid measures, which tend to be eventually loosened or abandoned due to economic losses, delay the peak of infection and fail to reduce the total number of cases. Our calculations relate the pandemic's second wave to high seasonal fluctuations and a low vulnerability stratification coefficient. Our characterisation of basic reproduction dynamics indicates that second wave of the pandemic is likely to first occur in Germany, Spain, France, and Italy, and a second wave is also possible in the U.K. and the U.S. Our findings show that even if the total elimination of the virus is impossible, the total number of infected people can be reduced during the deceleration stage.

Evidence of integrated health service delivery during COVID-19 in low and lower-middle-income countries: protocol for a scoping review.

INTRODUCTION: The importance of integrated, people-centred health systems has been recognised as a central component of Universal Health Coverage. Integration has also been highlighted as a critical element for building resilient health systems that can withstand the shock of health emergencies. However, there is a dearth of research and systematic synthesis of evidence on the synergistic relationship between integrated health services and pandemic preparedness, response, and recovery in low-income and lower-middle-income countries (LMICs). Thus, the authors are organising a scoping review aiming to explore the application of integrated health service delivery approaches during the emerging COVID-19 pandemic in LMICs. METHODS AND ANALYSIS: This scoping review adheres to the six steps for scoping reviews from Arksey and O'Malley. Peer-reviewed scientific literature will be systematically assembled using a standardised and replicable search strategy from seven electronic databases, including PubMed, Embase, Scopus, Web of Science, CINAHL Plus, the WHO's Global Research Database on COVID-19 and LitCovid. Initially, the title and abstract of the collected literature, published in English from December 2019 to June 2020, will be screened for inclusion which will be followed by a full-text review by two independent reviewers. Data will be charted using a data extraction form and reported in narrative format with accompanying data matrix. ETHICS AND DISSEMINATION: No ethical approval is required for the review. The study will be conducted from June 2020 to May 2021. Results from this scoping review will provide a snapshot of the evidence currently being generated related to integrated health service delivery in response to the COVID-19 pandemic in LMICs. The findings will be developed into reports and a peer-reviewed article and will assist policy-makers in making pragmatic and evidence-based decisions for current and future pandemic responses.

An interrupted time series analysis of the lockdown policies in India: a national-level analysis of COVID-19 incidence.

India implemented a national mandatory lockdown policy (Lockdown 1.0) on 24 March 2020 in response to Coronavirus Disease 2019 (COVID-19). The policy was revised in three subsequent stages (Lockdown 2.0-4.0 between 15 April to 18 May 2020), and restrictions were lifted (Unlockdown 1.0) on 1 June 2020. This study evaluated the effect of lockdown policy on the COVID-19 incidence rate at the national level to inform policy response for this and future pandemics. We conducted an interrupted time series analysis with a segmented regression model using publicly available data on daily reported new COVID-19 cases between 2 March 2020 and 1 September 2020. National-level data from Google Community Mobility Reports during this timeframe were also used in model development and robustness checks. Results showed an 8% [95% confidence interval (CI) = 6-9%] reduction in the change in incidence rate per day after Lockdown 1.0 compared to prior to the Lockdown order, with an additional reduction of 3% (95% CI = 2-3%) after Lockdown 4.0, suggesting an 11% (95% CI = 9-12%) reduction in the change in COVID-19 incidence after Lockdown 4.0 compared to the period before Lockdown 1.0. Uptake of the lockdown policy is indicated by decreased mobility and attenuation of the increasing incidence of COVID-19. The increasing rate of incident case reports in India was attenuated after the lockdown policy was implemented compared to before, and this reduction was maintained after the restrictions were eased, suggesting that the policy helped to 'flatten the curve' and buy additional time for pandemic preparedness, response and recovery.

Global Epidemiology of First 90 Days into COVID-19 Pandemic: Disease Incidence, Prevalence, Case Fatality Rate and Their Association with Population Density, Urbanisation and Elderly Population

As the COVID-19 pandemic marches exponentially, epidemiological data is of high importance to analyse the current situation and guide intervention strategies. This study analyses the epidemiological data of COVID-19 from 17 countries, representing 85 per cent of the total cases within first 90 days of lockdown in Wuhan, China. It follows a population-level observational study design and includes countries with 20,000 cases (or higher) as of 21 April 2020. We sourced the data for these 17 countries from worldometers. info, a digital platform being used by several media and reputed academic institutions worldwide. We calculated the prevalence, incidence, case fatality rate and trends in the epidemiology of COVID-19, and its correlation with population density, urbanisation and elderly population. The analysis represents 85 per cent (N= 2,183,661) of all cases within the first 90 days of the pandemic. Across the analysed period, the burden of the pandemic primarily focused on high- and middle-income countries of Asia, Europe and North America. While the total number of cases and deaths are highest in USA, the prevalence, incidence and case fatality rates are higher in the European countries. The prevalence and incidence vary widely among countries included in the analysis, and the number of cases per million and the case fatality rate are correlated with the proportion of the elderly population and to a lesser extent with the proportion of the urban population.